Innovative Support to Emergencies, Diseases, and Disasters

EVOLVE: INTEGRATED GLOBAL EARLY WARNING AND RESPONSE SYSTEM

Photo credit: IRMA (Integrated Risk Management for Africa)

AMIA Spring Congress Walt Disney World SwanMay 28th–30th, 2009, Orlando, Florida, USA Taha Kass-Hout,

MD, MSDirector, Global Public Health and Informatics

Overview

Infectious disease events represent substantial morbidity, mortality, and socio-economic impact

DAY

CA

SE

S

Opportunity for control

Late Detection – Response

DAY

CA

SE

S

Opportunity for control

Early Detection and Response

1000 Malaria infections (100%)

50 Malaria notifications (5%)

Get as close to the bottom of the pyramid

as possible

Urge frequent reporting: Weekly daily immediately

Specificity / Reliability

Sensitivity / Timeliness

• Main attributeso Representativenesso Completenesso Predictive value positive

Public Health Measures

Signal as early

as possible

Time

• Main attributeso Timeliness

Health care hotline

Public Health Measures

One of four major initiatives of the UN Millennium Action Plan (2000)

mHealth for Development: The Opportunity of Mobile Technology for Healthcare in the Developing World (2009)

Making Mobile Tech Work for Health

Photo Source: UN Foundation

SE Asia Region (Source: Wikipedia)

The Komphun rural Health Center serves over 7000 population in the Stung Treng and neighboring provinces.

Avian Influenza Exercise: Stung Treng Province, Cambodia, October 13-15, 2008

Cell phone use during the Avian Influenza Exercise: Stung Treng Province, Cambodia, October 13-15, 2008

Making Mobile Tech Work for Health

Growth of Mobile Technologies

Adapted from Dzenowagi, WHO, 2005

Internet penetration levels among the population as a whole

India 5.2% Malaysia 59.0% Thailand 20.5% Myanmar 0.1%

This compares to about 73.6% for North America

Some countries in Asia are also shown to be high such as Japan, S. Korea, Taiwan and Hong Kong

Nigel Collier, BioCaster: http://biocaster.nii.ac.jp Data Source: http://www.internetworldstats.com/stats3.htm#asia

Internet Penetration in Asia Pacific

UNCTAD Handbook of Statistics 2004

Urban – Rural Population, SE Asia

Adapted from Dzenowagi, WHO, 2005

Year: 2002

Our Approach

Hybrid human and machine-based

Collaborative and cross-disciplinary

Web 2.0/3.0 (geo-semantic) platform

Information Sources

Event-based ad-hoc unstructured reports issued by formal or informal sources

Indicator-based (number of cases, rates, proportion of strains…)

Timeliness, Representativeness, Completeness, Predictive Value, Quality, …

Evolve Architecture and Processes

Best Poster Award for Improving Public Health Investigation and Response at the Seventh Annual ISDS Conference, 2008http://kasshout.blogspot.com/2008/12/best-poster-award-for-improving-public.html

Feature extraction, reference and baseline information

Tags

Multiple Data Streams

User-Generated and Machine Learning Metadata

Comments

Spatio-temporal

Flags/Alerts/Bookmarks

Evo

lve Bo

tEvent Classification,

Characterization and Detection

Previous Event Training Data

Previous Event Control Data

Metadataextraction

Machine learning

Social network

Professional feedback

Anomaly detection

Collaborative Spaces

Hypotheses generation\testing

Evolve Architecture and Processes

Related items (e.g., News articles) are grouped into a thread. Threads are

later associated with events (hypothesized or confirmed).

Collaborative-centric

semantic tags

Evolve

Expert-generated

semantic tags

Publish and Share Information

Create a filter (by keyword, tag,

topic, location, or time) and

subscription (email, GeoRSS,

SMS Text Messaging,

Twitter, etc.)

An event is monitored through a

thread of items

Data source: SE Asia Evolve Collaborative Workspacehttp://riff.instedd.org/space/ProMed-MBDS

List view

Yin Myo Aye, MD: ProMED MBDSTaha Kass-Hout, MD, MS: InSTEDD

Expert-centric auto-generated

(machine-learning)

semantic tags and related

items

Evolve

Data source: SE Asia Evolve Collaborative Workspacehttp://riff.instedd.org/space/ProMed-MBDS

Tags are semantically ranked (a statistical possibility match). Users can further train the classifier by rejecting a suggestion. Users can also train the geo-locator by

rejecting or updating a location.

Yin Myo Aye, MD: ProMED MBDSTaha Kass-Hout, MD, MS: InSTEDD

EvolveMap view

Data source: SE Asia Evolve Collaborative Workspacehttp://riff.instedd.org/space/ProMed-MBDS

Semantic map to monitor topic rise or decay

over time

Yin Myo Aye, MD: ProMED MBDSTaha Kass-Hout, MD, MS: InSTEDD

Filter feature which automatically filters content

by topic of interest

Evolve

Filter content by

radius

Data source: SE Asia Evolve Collaborative Workspacehttp://riff.instedd.org/space/ProMed-MBDS

Yin Myo Aye, MD: ProMED MBDSTaha Kass-Hout, MD, MS: InSTEDD

Automatic Classification

Current classification includes: 7 syndromes 10 transmission modes > 100 infectious diseases > 180 micro-organisms > 140 symptoms > 50 chemicals

Indicators and Insights

Approximations of Epidemiological Features

Response Local Public Community Reaction (Public

and Responders) Infrastructure Infectious Disease Disaster

Snapshot: SE Asia, 2008-2009From September 1, 2008 to February 27, 2009 998 near real-time reports on

46 infectious diseases that effect humans or animals

Myanmar, Thailand, Laos, Cambodia, and Vietnam

220 provinces, 239 districts, and 14 cities

Data source: SE Asia Evolve Collaborative Workspacehttp://riff.instedd.org/space/ProMed-MBDS

Snapshot: SE Asia, 2008-2009From September 1, 2008 to February 27, 2009 The infectious disease event reporting in

SE Asia was of: Low socioeconomic disruption (83%), High socioeconomic disruption (17%); with

indicators of: potential sociological crisis (16.4%), and disaster (0.6%)

Data source: SE Asia Evolve Collaborative Workspacehttp://riff.instedd.org/space/ProMed-MBDS

Data source: Google Insights for Searchhttp://www.google.com/insights/search/#q=%22swine%20flu%22%2C%22avian%20flu%22&cmpt=q

Influenza A(H1N1), 2009

avian flu

avian flu

swine flu

swine flu

Data source: HashTags.org monitoring Twitterhttp://hashtags.org/tag/swineflu/messages

Influenza A(H1N1), 2009

Influenza A(H1N1), 2009

Data source: A(H1N1) Evolve Collaborative Workspacehttp://riff.instedd.org/space/SwineFlu

Yin Myo Aye, MD: ProMED MBDSTaha Kass-Hout, MD, MS: InSTEDD

Data source: A(H1N1) Evolve Collaborative Workspacehttp://riff.instedd.org/space/SwineFlu

Yin Myo Aye, MD: ProMED MBDSTaha Kass-Hout, MD, MS: InSTEDD

Influenza A(H1N1), 2009

Mid-March 2009 thru May 19th 2009

Data source: A(H1N1) Evolve Collaborative Workspacehttp://riff.instedd.org/space/SwineFlu

Influenza A(H1N1), 2009

Mid-March 2009 thru May 19th 2009

Yin Myo Aye, MD: ProMED MBDSTaha Kass-Hout, MD, MS: InSTEDD

Influenza A(H1N1), 2009

Data source: A(H1N1) Evolve Collaborative Workspacehttp://riff.instedd.org/space/SwineFlu

Mid-March 2009 thru May 19th 2009

Yin Myo Aye, MD: ProMED MBDSTaha Kass-Hout, MD, MS: InSTEDD

Avian Influenza: Egypt, 2009

Tracking the recent Avian Influenza

Outbreak in Egypt (reports started to

appear late January 2009).

Tracking the recent Avian Influenza

Outbreak in Egypt (reports started to

appear late January 2009).

Data source: Africa Evolve Collaborative Workspacehttp://riff.instedd.org/space/AfricaAlerts

Worldwide Health Events, 2008

Data source: Early Detection and Response Evolve Collaborative Workspacehttp://riff.instedd.org/space/DEMOEventDetection

Acknowledgment

Through Funding from…

InSTEDD400 Hamilton Avenue, Suite 120

Palo Alto, CA 94301, USA

+1.650.353.4440

+1.877.650.4440 (toll-free in the US)

info@instedd.org

Thank You!

Cambodia, Photo taken by Taha Kass-Hout, October 2008

“this pic says it all- our kids are all the same- they deserve the same”, Comment by Robert Gregg on Facebook, October 2008